QSVM vs Classical SVM for Classification BenchmarksQuantum Kernels Against Classical Baselines
Audit a 569-sample QSVM benchmark against classical SVM.
Compare Quantum Kernels with a Classical SVM Baseline
Comparison Setup
A classical SVM separates classes by maximizing the margin in feature space. The source QSVM use case uses a binary breast-cancer classification benchmark with 569 samples and 30 FNA-derived features, scaled to the quantum feature range.
QSVM keeps the SVM pipeline but uses a quantum kernel: inputs are encoded into quantum states and the kernel is estimated from state overlaps. In the PoC, the quantum-kernel run uses 4 qubits and 2 layers, then reports accuracy, balanced accuracy, Macro F1, and ROC-AUC against classical baselines.
- • Does the QSVM run reach the reported 96% accuracy while preserving class balance?
- • Which preprocessing, feature map, 4-qubit encoding, and 2-layer circuit were used?
- • Are results reproducible with fixed seeds and documented simulator assumptions?
Why compare them
- Classical SVM gives a familiar baseline with well-understood margin and kernel behavior.
- QSVM replaces the kernel evaluation with quantum-state overlaps to test whether the embedding helps the task.
- A shared benchmark prevents quantum-kernel claims from being evaluated without a classical reference.
Limits to keep explicit
- Quantum-kernel improvements are not guaranteed and depend on the dataset and feature map.
- Current devices add gate and measurement noise that can degrade classification quality.
- Simulator runs avoid hardware noise but add their own computational cost and scaling limits.
Comparison Outputs
The source PoC reports 96% accuracy, 95% balanced accuracy, 0.95 Macro F1, and 0.99 ROC-AUC on the simulator run.
Dataset
569 samples
Features
30 FNA features
Quantum kernel
4 qubits, 2 layers
Accuracy
96%
Who should use this comparison
Use it when a quantum-kernel result needs a clear classical SVM baseline before it is trusted.
569
Breast-cancer samples
30
FNA-derived features
4 qubits
Quantum-kernel width
0.99
Reported ROC-AUC
ML benchmark teams
For teams that need the same data split, preprocessing, and metrics across quantum and classical kernels.
Applied quantum researchers
For researchers testing whether a quantum feature map changes classification quality.
Product and R&D leads
For teams deciding whether a QSVM result is strong enough to justify further experiments.
How it works
One dataset, two kernel pipelines, one reproducible comparison.
Prepare Data
Use the same split, scaling, labels, and 30-feature FNA dataset for both SVM variants
Run SVM
Train the classical SVM baseline and record the core classification metrics
Run QSVM
Encode features into a 4-qubit, 2-layer quantum kernel and evaluate state overlaps
Compare
Review 96% accuracy, 95% balanced accuracy, 0.95 Macro F1, 0.99 ROC-AUC, and assumptions
Export
Download the methods, plots, code, and reproducible benchmark report
Related pages
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Comparisons
HQNN vs Classical Neural Network
Compare hybrid quantum neural networks with classical MLP and CNN baselines on parameters, training time, and accuracy across three benchmarked use cases.
Benchmark QSVM Against Classical SVM
Run the 569-sample QSVM benchmark and review 96% accuracy, 95% balanced accuracy, 0.95 Macro F1, and 0.99 ROC-AUC.
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